1. Field of the Invention
The present invention relates to measurement of interference in wireless networks, and more particularly, to systems and methods for precisely estimating interference levels wherein the interfering signals are not necessarily time-synchronized with the receiver performing the interference measurements.
2. Description of Related Art
In TDMA (Time Division Multiple Access) wireless networks such as GSM (Global System for Mobile Communications) and IS-136 based wireless networks, different mobile stations in the coverage area of a particular base station may transmit using the same physical channel, as defined by a given frequency or frequency hopping sequence. However, the signals for these mobile stations are transmitted in different time slots, thereby defining logical channels.
Mobile stations in the coverage area of different base stations may also transmit using the same physical channel, according to the principles of frequency reuse. Channel reuse allows carriers to efficiently utilize spectrum, but often presents the problem of co-channel interference. Co-channel interference occurs when the signal transmitted by a mobile station is corrupted with one or more signals transmitted by other mobile stations on the same physical channel at the same time. If the level of co-channel interference is excessive, call quality is degraded and the probability that calls will be dropped increases. Thus, measurement of interference levels, commonly calculated as the received signal strength on an idle channel, is very useful to aid in network design and interference avoidance techniques.
In particular, precise measurement of interference levels is critical to automated traffic channel selection algorithms that are commonly implemented in the wireless infrastructure. In response to a channel request emanating from a mobile station, an automated traffic channel selection algorithm controlled by the base station and/or Mobile Switching Center uses interference levels measured on different idle channels to determine the optimum channel to allocate to the wireless call, thereby increasing efficient use of radio spectrum and improving system performance.
According to one approach, interference is estimated on a particular physical channel by measuring the interference level on each of the time slots associated with the physical channel, and then, by determining the maximum of these measurements. The interference measurement on a time slot can be performed by taking a linear or logarithmic average of the energy within each bit or symbol of the time slot. This process is problematic, however, particularly in asynchronous networks. Because interfering signals are not necessarily time synchronized with the receiver performing the interference measurement, the idle time slot of the receiver performing the interference measurement may overlap the time slots of two different interferers in two separate time slots on another base station. Thus, interference measurements of the measured timeslot may include interference from one or both of the interferers. It is impossible to predict how far the interference from such an unsynchronized interferer would extend into the time slot in which the receiver is performing interference measurement (the measured time slot). Hence, the interference measurement would not necessarily reflect the peak interference experienced within the time slot. This problem may result in a significant underestimation of the interference level, particularly when the burst of the interfering mobile overlaps multiple time slots of the receiver performing interference measurement and the receiver detects no interference on the remaining parts of the measured time slot.
As an attempt to address the foregoing concern, each measured time slot can be divided into n segments. Interference measurements are made for each segment, and then the average of these interference measurements is selected as the interference for the time slot. Again, due to the potentially asynchronous nature of the transmissions, some segments of a time slot may not experience any interference, because the measured time slot could overlap multiple interferers' time slots, or include multiple bursts, or include noise and an interfering burst.
Consider the following Example 1. FIG. 2 is a simplified block diagram illustrating the relationship of the time slots of two channels, each allocated to a different base station. Frame 202 from a first base station includes four time slots, as does frame 204 from a second base station, but the time slots are not synchronized. A mobile station is transmitting via the first base station during time slot 1 of frame 202. Assume that the goal is to measure the interference caused by that transmitting mobile station, and that the measurement interval is the logical channel represented by time slot 2 of frame 204. If each time slot of frame 204 is divided into 10 segments (as shown in FIG. 3), using the starting position of time slot 1 in frame 202 as the reference point K, three segments of the measured time slot 2 have experienced no interference. To the right of reference point K, the remaining seven segments of the measured time slot 2 experience interference caused by the overlapping portion of the transmission occurring during timeslot 1 of frame 202. Another portion of the transmission overlaps a portion of time slot 3 of frame 204. When the interference for the time slot 2 of frame 204 is calculated, the calculation includes three segments with ambient noise and seven segments with interference measurements due to the signal transmission occurring during time slot 1 of frame 202. Due to the asynchronous nature of TDMA and GSM networks, the measuring receiver does not know which segments contain interference measurements. Both a linear and a logarithmic average of interference measurements across these n segments would yield lower interference estimates than the actual interference experienced at least for some of the symbols in the time slot.
In alternative embodiments in the prior art, weighting algorithms are commonly applied prior to calculating the maximum, whereby each of the n segments is afforded a weight that is proportional to the relative importance of the symbols included in that segment. For example, the initial segments of a time slot may typically include control bits, and may therefore be given lower weight than the data bits. In this scenario, weighting the segments before estimating the interference level is also problematic in asynchronous networks where the interfering burst is not necessarily time aligned, due to the potential for applying the wrong weight to a particular symbol. It cannot be predicted where the control bits (as well as data bits or any other important bits) of the interfering signal would be located within a time slot of the receiver performing interference measurement. The location of data bits and control bits within a time slot of the receiver performing interference measurement are known. However, the interfering burst may not overlap all the data bits and hence, the interference measured for all the data bits would still yield a lower interference estimate than the actual interference experienced by at least some of the data bits.
Thus, current methods for interference estimation in asynchronous networks are not accurate. The segmentation and weighting protocols described above does not yield the accurate interference measurement experienced on a physical or logical channel. The following examples further illustrate the shortcomings of current methods for interference measurement.
Example 2 refers again to FIGS. 2 and 3, where the measured time slot is time slot 2 of frame 204. Suppose the data bits are in segments 3 through 10 of the measured time slot, control bits are in segments 1 through 3 of the measured time slot, control bits are given weight of zero, an interfering burst occurs on segments 6 through 10 at −90 dBm, and segments 1 through 5 measure noise at −116 dBm. The resulting interference estimation yields:
            [                        (                      3            ⋆                          (                              -                116                            )                                )                +                  (                      5            ⋆                          (                              -                90                            )                                )                    ]        8    =            -      100        ⁢    dBm  
If this channel is allocated to a call, 62.5% of the data bits would experience interference at −90 dBm, i.e. 10 dB higher than the interference estimated by such an algorithm. Changing the weights would not result in accurate estimation of the interference.
Example 3 also refers to FIGS. 2 and 3, where the measured time slot is time slot 2 of frame 204. Suppose that a −100 dBm interfering burst is present across segments 2 through 10. The other assumptions remain the same as in Example 2. The resulting interference estimation yields:
            [              8        ⋆                  (                      -            116                    )                    ]        8    =            -      100        ⁢    dBm  
The interference in this case would also be estimated as −100 dBm, which is the same as the interference estimated for Example 1, in which the interference was as high as −90 dBm. Therefore, conventional methods for interference estimation can yield the same interference measurement for quite different interference conditions.
As wireless service providers focus their efforts on increasing the quality of service provided to wireless customers, accurate interference measurements will be imperative for the efficient utilization of radio frequency spectrum of wireless networks. Underestimating the interference level may result in a problem area going unresolved, potentially allocating calls to frequencies with higher amounts of interference, and possibly, creating customer dissatisfaction and churn. Overestimating can cause the carrier to fail to reuse frequencies as efficiently as possible, thereby addition to network costs. Therefore, there is a need in the art for a more accurate approach for estimating interference level in non-synchronized networks.